Range Model of Electric Vehicles With Multi-Speed Transmissions
Bibliographic record
Abstract
Range-prediction models of electric vehicles (EVs) are essential for vehicle designers because range is still a major problem in EVs. Most range models are only available for EVs with fixed gearing. However, recent developments in EVs incorporate multi-speed transmissions (MSTs). Furthermore, transmissions are modeled only with a constant efficiency in most EV range-prediction simulation results available in the literature. For this reason, a simple and accurate range model for EVs with MSTs is proposed in this paper. In order to predict the range of EVs with MSTs accurately, the transmission efficiency is estimated by means of the transmission mathematical model. The efficiency results are verified with a comprehensive model that has been validated experimentally. A case study pertaining to the GM EV1 with a two-speed novel modular transmission is provided. Moreover, simulation results under constant efficiency are included to show the advantages of the proposed model in range-prediction. Our simulation results show that a more accurate range-prediction can be obtained by means of the proposed model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".